autosearch:recent-signal-fusion

Recent Signal Fusion — Cross-Platform Recency Bundle

Adapted from last30days-skill's SourceItem / Candidate / Cluster / Report pipeline + Scira's group-mode aggregation. Turns N channels' recent outputs into a single time-weighted cluster list.

Input

input:
  topic: str                                 # research topic
  time_window: "24h" | "7d" | "30d"          # how far back to care
  channels: list[str]                        # which channel skills to call
  min_recency_ratio: float                   # reject items older than (now - time_window)
  max_items_per_channel: int                 # e.g. 30

Pipeline Stages

1. Parallel fetch — call each channel with a time-window filter in the query. Collect raw evidence lists.

1. Normalize to SourceItems:

``yaml source_item: id: str # hash(url + title) url: str title: str content_snippet: str platform: str # source_channel posted_at: datetime engagement: {likes, comments, shares} | null language: str raw_evidence_ref: dict # original Evidence slim-dict ``

1. Recency filter — drop items older than time_window. Use extract-dates skill for items without explicit posted_at.

1. Semantic clustering — group near-duplicate SourceItems across platforms:

``yaml cluster: id: str canonical_title: str # best representative title topic_keywords: list[str] # extracted from the cluster sources: list[source_item_id] platform_spread: int # how many distinct platforms earliest_posted_at: datetime latest_posted_at: datetime ``

1. Rank by weighted score:

``` score = ( 0.4 * recency_score(latest_posted_at, time_window)

• 0.3 * platform_spread_score(platform_spread) # cross-platform = stronger signal
• 0.2 * engagement_score(sum(engagement)) # aggregated across sources
• 0.1 * platform_reliability_score(platforms) # quality weighting

) ```

1. Emit bundle:

``yaml bundle: topic: str time_window: str total_source_items: int total_clusters: int top_clusters: list[Cluster] # top 20 by score platforms_hit: list[str] platforms_empty: list[str] earliest_signal: datetime latest_signal: datetime ``

Platform Reliability Default Weights

• github-* / arxiv / hackernews → 1.0 (deterministic, low spam).
• stackoverflow / reddit / devto → 0.8 (moderated, some spam).
• twitter-exa / xiaohongshu / weibo / douyin → 0.6 (high spam, high recency value).
• search-rss (user-curated) → 1.0.

Runtime AI can override per-session for specific platform-topic fits.

Anti-Collapse Guard

If after ranking, the top 5 clusters are all from the same platform → flag as "platform-collapse", re-rank with platform_spread weighted 2×. Ensures the bundle isn't just "top 5 trending X posts" when other platforms had relevant signal.

When to Use

• User asks "what's happening with X recently?" / "what's the latest on X?" / "近期 X 讨论怎么样?"
• Weekly / daily brief generation.
• Trend tracking across platforms.

When NOT to Use

• Historical / reference research (use channels-academic, not this).
• Single-platform watch (call that channel directly).
• Highly technical API questions (recency not the primary filter).

Cost

Standard-tier LLM for clustering + canonical-title extraction. Typical run: 5-10 seconds + sum of channel call costs. Cheaper than decompose-task because channels fan out in parallel.

Interactions

• Calls → run_channel (parallel) for each selected channel.
• Uses → extract-dates (normalize posted_at fields).
• Uses → rerank-evidence (as fallback when semantic clustering is inconclusive).
• Feeds → synthesize-knowledge (the bundle is the input context).

Quality Bar

• Evidence items have non-empty title and url.
• No crash on empty or malformed API response.
• Source channel field matches the channel name.